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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(5): 454-459 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.5.728

Multi-class Classification of Gene Expression Data Using Deep Learning for Cancer Prediction

Soumiya Hamena and Souham Meshoul
Abstract—Deep Learning is a machine learning model that has shown superior performance for a wide range of application. Deep learning has revived the old domain of artificial neural networks and allowed to renew with connexionism in an unprecedented and strongest way. In fact, Deep Learning has advanced rapidly since the early 2000s in various fields and have provided remarkable results in various machine learning applications, including speech recognition, computer vision, and natural language processing. Accordingly, Deep Learning algorithms are promising avenue of research in the automated extraction of complex data representations at high levels of abstraction. By another way, one of the major challenges in bioinformatics is the construction of accurate classification models based on huge and high dimensional data sets such as gene expression data. Gene expression is evaluated by measuring the number of RNA transcripts in a tissue sample. Cancer classification using gene expression data is applied to solve the problems relating to cancer diagnosis and drug discovery. In this paper, we propose an approach that proposes a deep learning based model to achieve multi-class classification of gene expression data with the aim to predict the type of cancer. The validation of the approach is achieved using Keras platform. Very encouraging results have been obtained..

Index Terms—Gene expression, deep learning, machine learning, neural networks, bioinformatics, Keras.

The authors are with Computer Science and Application Department/FNTIC, Constantine, Algeria (e-mail: Soumiya.hamena@univ-constantine2.dz, Souham.meshoul@univ-constantine2.dz).


Cite: Soumiya Hamena and Souham Meshoul, "Multi-class Classification of Gene Expression Data Using Deep Learning for Cancer Prediction," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 454-459, 2018.

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